Influence of Soil Strength on Optimal Embedment Depth of Geosynthetic Reinforcement Layer in Granular-Reinforced Foundations
Bibliographic record
Abstract
The effect of soil friction angle value on the optimal embedment depth of a single geosynthetic reinforcement layer in granular foundations was investigated with a numeric simulation approach. The numeric model was validated against the measured results of reduced-scale plane strain model foundations tested with two footing width sizes. The model foundations were constructed with gravel-sized round aggregates and were subjected to the concentrated vertical load of shallow footings analogous to a track–ballast system. After the validation of the numeric model, a series of parametric analyses was carried out with an idealized, full-scale foundation model to investigate the dependency of the optimal reinforcement embedment depth on the soil friction angle value. The optimal reinforcement depth in field-scale reinforced foundations under strip footings is between 10% and 15% of footing width, depending on the site soil friction angle value. The optimal reinforcement depth is found to be slightly closer to the footing in a weaker soil. The range of optimum reinforcement depth values obtained from this study is shallower than those suggested in some earlier studies using reduced-scale model foundations. Optimum reinforcement depth values obtained merely on the basis of the results of reduced-scale tests may be overestimated because of the unrealistically low overburden pressure compared with the field-scale foundations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".